Neural networks can be distinguished into distinct types based on the architecture. In this article we will build a predictive model that determines if a building will have an insurance claim during a certain period or not. was the most common category, unfortunately). Data. Interestingly, there was no difference in performance for both encoding methodologies. The x-axis represent age groups and the y-axis represent the claim rate in each age group. (2011) and El-said et al. A tag already exists with the provided branch name. insurance claim prediction machine learning. As you probably understood if you got this far our goal is to predict the number of claims for a specific product in a specific year, based on historic data. According to Rizal et al. The models can be applied to the data collected in coming years to predict the premium. It also shows the premium status and customer satisfaction every . of a health insurance. Are you sure you want to create this branch? Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Claim rate, however, is lower standing on just 3.04%. by admin | Jul 6, 2022 | blog | 0 comments, In this 2-part blog post well try to give you a taste of one of our recently completed POC demonstrating the advantages of using Machine Learning (read here) to predict the future number of claims in two different health insurance product. We found out that while they do have many differences and should not be modeled together they also have enough similarities such that the best methodology for the Surgery analysis was also the best for the Ambulatory insurance. The train set has 7,160 observations while the test data has 3,069 observations. If you have some experience in Machine Learning and Data Science you might be asking yourself, so we need to predict for each policy how many claims it will make. Predicting the cost of claims in an insurance company is a real-life problem that needs to be , A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. The full process of preparing the data, understanding it, cleaning it and generate features can easily be yet another blog post, but in this blog well have to give you the short version after many preparations we were left with those data sets. Dr. Akhilesh Das Gupta Institute of Technology & Management. Later the accuracies of these models were compared. However, training has to be done first with the data associated. Coders Packet . These claim amounts are usually high in millions of dollars every year. In health insurance many factors such as pre-existing body condition, family medical history, Body Mass Index (BMI), marital status, location, past insurances etc affects the amount. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Dong et al. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, And the total number of predicted claims will be, $$True \: positive\:+\: False\: positive \: = 4,500\:+\:1,125 = 5,625$$, This seems pretty close to the true number of claims, 5,000, but its 12.5% higher than it and thats too much for us! This Notebook has been released under the Apache 2.0 open source license. Using the final model, the test set was run and a prediction set obtained. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Machine learning can be defined as the process of teaching a computer system which allows it to make accurate predictions after the data is fed. Introduction to Digital Platform Strategy? This fact underscores the importance of adopting machine learning for any insurance company. Refresh the page, check. This amount needs to be included in This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Model performance was compared using k-fold cross validation. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. can Streamline Data Operations and enable It also shows the premium status and customer satisfaction every month, which interprets customer satisfaction as around 48%, and customers are delighted with their insurance plans. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. arrow_right_alt. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. Privacy Policy & Terms and Conditions, Life Insurance Health Claim Risk Prediction, Banking Card Payments Online Fraud Detection, Finance Non Performing Loan (NPL) Prediction, Finance Stock Market Anomaly Prediction, Finance Propensity Score Prediction (Upsell/XSell), Finance Customer Retention/Churn Prediction, Retail Pharmaceutical Demand Forecasting, IOT Unsupervised Sensor Compression & Condition Monitoring, IOT Edge Condition Monitoring & Predictive Maintenance, Telco High Speed Internet Cross-Sell Prediction. HEALTH_INSURANCE_CLAIM_PREDICTION. Appl. Multiple linear regression can be defined as extended simple linear regression. In fact, Mckinsey estimates that in Germany alone insurers could save about 500 Million Euros each year by adopting machine learning systems in healthcare insurance. (2011) and El-said et al. In this learning, algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The mean and median work well with continuous variables while the Mode works well with categorical variables. Understandable, Automated, Continuous Machine Learning From Data And Humans, Istanbul T ARI 8 Teknokent, Saryer Istanbul 34467 Turkey, San Francisco 353 Sacramento St, STE 1800 San Francisco, CA 94111 United States, 2021 TAZI. (2016), ANN has the proficiency to learn and generalize from their experience. for the project. Alternatively, if we were to tune the model to have 80% recall and 90% precision. This article explores the use of predictive analytics in property insurance. We already say how a. model can achieve 97% accuracy on our data. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Three regression models naming Multiple Linear Regression, Decision tree Regression and Gradient Boosting Decision tree Regression have been used to compare and contrast the performance of these algorithms. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. So cleaning of dataset becomes important for using the data under various regression algorithms. Notebook. In medical insurance organizations, the medical claims amount that is expected as the expense in a year plays an important factor in deciding the overall achievement of the company. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. At the same time fraud in this industry is turning into a critical problem. Factors determining the amount of insurance vary from company to company. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. As a result, we have given a demo of dashboards for reference; you will be confident in incurred loss and claim status as a predicted model. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. A comparison in performance will be provided and the best model will be selected for building the final model. history Version 2 of 2. Previous research investigated the use of artificial neural networks (NNs) to develop models as aids to the insurance underwriter when determining acceptability and price on insurance policies. The main application of unsupervised learning is density estimation in statistics. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. Early health insurance amount prediction can help in better contemplation of the amount needed. It would be interesting to see how deep learning models would perform against the classic ensemble methods. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Save my name, email, and website in this browser for the next time I comment. According to Zhang et al. 2 shows various machine learning types along with their properties. The diagnosis set is going to be expanded to include more diseases. The data included some ambiguous values which were needed to be removed. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. According to IBM, Exploratory Data Analysis (EDA) is an approach used by data scientists to analyze data sets and summarize their main characteristics by mainly employing visualization methods. Claim rate is 5%, meaning 5,000 claims. Pre-processing and cleaning of data are one of the most important tasks that must be one before dataset can be used for machine learning. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . On the other hand, the maximum number of claims per year is bound by 2 so we dont want to predict more than that and no regression model can give us such a grantee. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. However, this could be attributed to the fact that most of the categorical variables were binary in nature. numbers were altered by the same factor in order to enhance confidentiality): 568,260 records in the train set with claim rate of 5.26%. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. The network was trained using immediate past 12 years of medical yearly claims data. Each plan has its own predefined . 1 input and 0 output. The distribution of number of claims is: Both data sets have over 25 potential features. By filtering and various machine learning models accuracy can be improved. Insurance Claims Risk Predictive Analytics and Software Tools. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. (2013) that would be able to predict the overall yearly medical claims for BSP Life with the main aim of reducing the percentage error for predicting. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Keywords Regression, Premium, Machine Learning. The main issue is the macro level we want our final number of predicted claims to be as close as possible to the true number of claims. Removing such attributes not only help in improving accuracy but also the overall performance and speed. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. The attributes also in combination were checked for better accuracy results. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The data has been imported from kaggle website. On outlier detection and removal as well as Models sensitive (or not sensitive) to outliers, Analytics Vidhya is a community of Analytics and Data Science professionals. Predicting the cost of claims in an insurance company is a real-life problem that needs to be solved in a more accurate and automated way. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. Dataset was used for training the models and that training helped to come up with some predictions. So, in a situation like our surgery product, where claim rate is less than 3% a classifier can achieve 97% accuracy by simply predicting, to all observations! Predicting the cost of claims in an insurance company is a real-life problem that needs to be solved in a more accurate and automated way. (2016) emphasize that the idea behind forecasting is previous know and observed information together with model outputs will be very useful in predicting future values. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. "Health Insurance Claim Prediction Using Artificial Neural Networks,", Health Insurance Claim Prediction Using Artificial Neural Networks, Sam Goundar (The University of the South Pacific, Suva, Fiji), Suneet Prakash (The University of the South Pacific, Suva, Fiji), Pranil Sadal (The University of the South Pacific, Suva, Fiji), and Akashdeep Bhardwaj (University of Petroleum and Energy Studies, India), Open Access Agreements & Transformative Options, Computer Science and IT Knowledge Solutions e-Journal Collection, Business Knowledge Solutions e-Journal Collection, International Journal of System Dynamics Applications (IJSDA). In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. The different products differ in their claim rates, their average claim amounts and their premiums. 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